Skip to main content
SpringerLink
Log in

Robust tracking via monocular active vision for an intelligent teaching system

  • Original Article
  • Published:
The Visual Computer Aims and scope Submit manuscript

Abstract

The research of this paper investigates a practical intelligent tracking teaching system, addressing the problem of teacher detection and tracking via monocular active vision in real time. The split lines and position-based visual servo rules are created to realize the robust and stable tracking, which is designed to keep the tracked teacher in the middle of image with a fixed size by automatically controlling a pan/tilt/zoom monocular camera in either rostrum region or other regions in the classroom. Face tracking in rostrum region is initiated by a face detector based on Adaboost followed by a novel long-term tracking algorithm named as informative random fern-tracking-learning-detection (IRF-TLD), which has advantages for its high accuracy and low memory requirement using real-valued feature and Gaussian random projection. Moreover, Gaussian mixture model can be automatically started to detect the teacher’s movement when face tracking fails or stand-up students are detected. Experimental results on many benchmark sequences, which include various challenges for tracking, such as occlusion, illumination and pose variations, and scaling, have demonstrated the superior performance of the proposed IRF-TLD method when compared with several state-of-the-art tracking algorithms. Extensive experiments in a series of challenging real classroom scenarios also demonstrate the effectiveness of the complete system.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11

Similar content being viewed by others

References

  1. Tsuruoka, S., Yamaguchi, T., Kato, K., Yoshikawa, T., Shinogi, T.: A camera control based on fuzzy behavior recognition of lecturer for distance lecture. In: 10th IEEE International Conference on Fuzzy Systems, pp. 940–943 (2001)

  2. Ruiguo, Y., Xinrong, Z.: The design and implementation of face tracking in real time multimedia recording system. In: 2nd International Congress on Image and Signal Processing (CISP), pp. 1–3 (2009)

  3. Wulff, B., Rolf, R.: Opentrack-automated camera control for lecture recordings. In: IEEE International Symposium on Multimedia (ISM), pp. 549–552 (2011)

  4. Rui, W., Lei, M.: Intelligent Tracking Teaching System based on monocular active vision. In: IEEE International Conference on Imaging Systems and Techniques (IST), pp. 431–436 (2013)

  5. Wang, R., Wang, Y.Y., Wang, L., Chen, X.Q., Zhu, S.P.: Robust and automatic tracking method of infrared extended object based on EM-like algorithm. Infrared Laser Eng. 37(4), 616–620 (2008)

    MathSciNet  Google Scholar 

  6. Kalal, Z., Mikolajczyk, K., Matas, J.: Tracking-learning-detection. IEEE Trans. Pattern Anal. Mach. Intell. 34(7), 1409–1422 (2012)

    Article  Google Scholar 

  7. Zhang, J., Liu, K., Cheng, F., Li, Y.: Visual tracking with randomly projected ferns. Signal Process. Image Commun. 29(9), 987–997 (2014)

    Article  Google Scholar 

  8. Bouguet, J.-Y.: Pyramidal implementation of the lucas kanade feature tracker description of the algorithm. Intel Corp. Microprocess. Res. Labs Tech. Rep. (2000)

  9. Ji, H.: Real time robust L1 tracker using accelerated proximal gradient approach. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1830–1837 (2012)

  10. Ross, D.A., Lim, J., Lin, R.-S., Yang, M.-H.: Incremental learning for robust visual tracking. Int. J. Comput. Vision. 77(1–3), 125–141 (2008)

    Article  Google Scholar 

  11. Xue, M., Ling, H.: Robust visual tracking and vehicle classification via sparse representation. IEEE Trans. Pattern Anal. Mach. Intell. 33(11), 2259–2272 (2011)

    Article  Google Scholar 

  12. Jia, X., Lu, H., Yang, M.-H.: Visual tracking via adaptive structural local sparse appearance model. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1822–1829 (2012)

  13. Zhong, W., Lu, H., Yang, M.-H.: Robust object tracking via sparsity-based collaborative model. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1838–1845 (2012)

  14. Hare, S., Saffari, A., Torr, P.H.: Struck: Structured output tracking with kernels. In: IEEE International Conference on Computer Vision (ICCV), pp. 263–270 (2011)

  15. Grabner, H., Grabner, M., Bischof, H.: Real-Time Tracking via On-line Boosting. In: British Machine Vision Conference (BMVC), pp. 47–56 (2006)

  16. Wang, Z., Yoon, S., Xie, S.J., Lu, Y., Park, D.S.: Visual tracking with semi-supervised online weighted multiple instance learning. Vis. Comput. pp. 1–14 (2015)

  17. Wang, N., Li, S., Gupta, A., Yeung, D.Y.: Transferring Rich Feature Hierarchies for Robust Visual Tracking. Eprint Arxiv (2015)

  18. Henriques, J.O.F., Caseiro, R., Martins, P., Batista, J.: High-Speed Tracking with Kernelized Correlation Filters. IEEE Trans. Pattern Anal. Mach. Intell. 37(3), 1–1 (2014)

    Google Scholar 

  19. Zhang, K., Zhang, L., Liu, Q., Zhang, D., Yang, M.H.: Fast Visual Tracking via Dense Spatio-temporal Context Learning. In: European Conference on Computer Vision (ECCV), pp. 127–141. Springer International Publishing (2014)

  20. Kalal, Z., Mikolajczyk, K., Matas, J.: Face-tld: Tracking-learning-detection applied to faces. In: 17th IEEE International Conference on Image Processing (ICIP), pp. 3789–3792 (2010)

  21. Wu, Y., Lim, J., Yang, M.-H.: Online object tracking: A benchmark. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 2411–2418 (2013)

  22. Zafeiriou, S., Zhang, C., Zhang, Z.: A survey on face detection in the wild: Past, present and future. Comput. Vision Image Underst. 138, 1–24 (2015)

    Article  Google Scholar 

  23. Zhao, W.Y., Chellappa, R., Phillips, P.J., Rosenfeld, A.: Face recognition: A literature survey. ACM Comput. Surv. 35, 399–458 (2003)

    Article  Google Scholar 

  24. Al Haj, M., Bagdanov, A.D., Gonzalez, J., Roca, F.X.: Reactive object tracking with a single PTZ camera. In: International Conference on Pattern Recognition (ICPR), pp. 1690–1693 (2010)

  25. Feng, P., Xuanyin, W., Quanqi, W.: Moving object tracking research based on active vision. In: Fifth World Congress on Intelligent Control and Automation (WCICA), pp. 3846–3849 (2004)

  26. Chen, H., Zhao, X., Tan, M.: A novel pan-tilt camera control approach for visual tracking. In: 11th World Congress on Intelligent Control and Automation (WCICA), pp. 2860–2865 (2014)

  27. Bernardin, K., Van De Camp, F., Stiefelhagen, R.: Automatic person detection and tracking using fuzzy controlled active cameras. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1–8 (2007)

  28. Reynolds, D.A., Quatieri, T.F., Dunn, R.B.: Speaker verification using adapted Gaussian mixture models. Digital Signal Process. 10(1), 19–41 (2000)

    Article  Google Scholar 

  29. Viola, P., Jones, M.: Rapid object detection using a boosted cascade of simple features. In: IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR), pp. 511–518 (2001)

  30. Zhang, K., Zhang, L., Yang, M.-H.: Real-time compressive tracking. In: 12th European Conference on Computer Vision (ECCV), pp. 864–877 (2012)

  31. Ozuysal, M., Fua, P., Lepetit, V.: Fast keypoint recognition in ten lines of code. In: IEEE Conference on Computer Vision and Pattern Recognition(CVPR), pp. 1–8 (2007)

  32. Achlioptas, D.: Database-friendly random projections: Johnson-Lindenstrauss with binary coins. J. Comput. Syst. Sci. 66(4), 671–687 (2003)

    Article  MathSciNet  MATH  Google Scholar 

  33. Baraniuk, R., Davenport, M., DeVore, R., Wakin, M.: A simple proof of the restricted isometry property for random matrices. Construct. Approx. 28(3), 253–263 (2008)

    Article  MathSciNet  MATH  Google Scholar 

  34. Wang, Z., Salah, M.B., Zhang, H.: Object joint detection and tracking using adaptive multiple motion models. Vis. Comput. 30(2), 173–187 (2014)

    Article  Google Scholar 

  35. Wu, Y., Jia, N., Sun, J.: Real-time multi-scale tracking based on compressive sensing. Vis. Comput. 31(4), 471–484 (2014)

    Article  Google Scholar 

  36. Kalal, Z., Matas, J., Mikolajczyk, K.: Pn learning: Bootstrapping binary classifiers by structural constraints. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 49–56 (2010)

  37. Liu, B., Huang, J., Yang, L., Kulikowsk, C.: Robust tracking using local sparse appearance model and k-selection. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1313–1320 (2011)

  38. Sevilla-Lara, L., Learned-Miller, E.: Distribution fields for tracking. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1910–1917 (2012)

  39. Zhang, K., Liu, Q., Wu, Y., Yang, M.-H.: Robust Tracking via Convolutional Networks without Learning. arXiv preprint arXiv:1501.04505 (2015)

Download references

Acknowledgments

The authors thank the anonymous reviewers for helping to review this paper. This work was partially supported by National Natural Science Foundation of China (60974108).

Author information

Authors and Affiliations

  1. Laboratory of Precision Opto-Mechatronics Technology, School of Instrumentation Science and Opto-Electronics Engineering, Beihang University, No.37 Xueyuan Road, Haidian District, Beijing, 100191, China

    Rui Wang & Hao Dong

  2. Department of Electrical and Computer Engineering, University of Missouri, Columbia, MO, 65211, USA

    Tony X. Han

  3. Software System Research Department, China Electronics Technology Group Corporation No. 38 Research Institute, No. 199 Xiangzhang Avenue, Hi-Tech Zone, Hefei, Anhui, 230088, China

    Lei Mei

Authors
  1. Rui Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Hao Dong
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Tony X. Han
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Lei Mei
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Hao Dong.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Wang, R., Dong, H., Han, T.X. et al. Robust tracking via monocular active vision for an intelligent teaching system. Vis Comput 32, 1379–1394 (2016). https://doi.org/10.1007/s00371-015-1206-8

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00371-015-1206-8

Keywords

Search

Navigation